Local dynamic integration of ensemble in prediction of time series
Local dynamic integration of ensemble in prediction of time series
复制标题
时间序列预测中系综的局部动态积分
DOI：
10.24425/bpasts.2019.129650
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
K. Siwek
中科院分区：
文献类型：
--
作者：
S. Osowski;K. Siwek
.本文提出了局部动态方法集成的一个合奏的预测。许多预测器结果的经典融合考虑到所有单元，并且采用形成系综的所有单元的结果的加权平均。本文提出了不同的方法。第二天的时间序列预测在这里仅由集合中的一个成员完成，该集合在输入向量的学习阶段中是最好的，最接近实际应用的输入数据。由于这样的安排，我们避免了最坏的单元降低整个合奏的准确性的情况。通过这种方式，我们获得了更高水平的统计预测准确性，因为每个任务都是由最适合的预测器执行的。此外，这种积分布置允许使用质量非常不同的单元，而不会降低最终预测的质量。通过对下一次输入、PM10平均污染和电力系统24要素时负荷预测的数值实验，验证了该方法的优越性。所有预报质量指标都有显著改进。
. The paper presents local dynamic approach to integration of an ensemble of predictors. The classical fusing of many predictor results takes into account all units and takes the weighted average of the results of all units forming the ensemble. This paper proposes different approach. The prediction of time series for the next day is done here by only one member of an ensemble, which was the best in the learning stage for the input vector, closest to the input data actually applied. Thanks to such arrangement we avoid the situation in which the worst unit reduces the accuracy of the whole ensemble. This way we obtain an increased level of statistical forecasting accuracy, since each task is performed by the best suited predictor. Moreover, such arrangement of integration allows for using units of very different quality without decreasing the quality of final prediction. The numerical experiments performed for forecasting the next input, the average PM10 pollution and forecasting the 24-element vector of hourly load of the power system have confirmed the superiority of the presented approach. All quality measures of forecast have been significantly improved.